Optimization and modeling of ZrB2 ceramic processing by EDM for high-performance industrial applications
This study investigates the Electrical Discharge Machining (EDM) of zirconium diboride (ZrB2), a novel conductive ceramic with exceptional properties, including high temperature resistance, excellent thermal conductivity, and remarkable hardness. These properties make ZrB2 highly suitable for extrem...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad Pública de Navarra |
| Repositorio: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra |
| OAI Identifier: | oai:academica-e.unavarra.es:2454/53978 |
| Acceso en línea: | https://hdl.handle.net/2454/53978 |
| Access Level: | acceso abierto |
| Palabra clave: | ZrB2 EDM Modeling Artificial neural network |
| Sumario: | This study investigates the Electrical Discharge Machining (EDM) of zirconium diboride (ZrB2), a novel conductive ceramic with exceptional properties, including high temperature resistance, excellent thermal conductivity, and remarkable hardness. These properties make ZrB2 highly suitable for extreme environments, such as aerospace and nuclear applications. To the best of our knowledge, no comprehensive studies have addressed the manufacturing of ZrB2 parts by EDM, positioning this research as a cutting-edge contribution. Two electrode materials, graphite (C) and copper-graphite (Cu–C), were used to analyze the material removal rate (MRR) and surface roughness (Ra) as functions of current intensity (I), pulse time (ti), and duty cycle (η). Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) were used to model the response variables. While MLR was effective for MRR (R2 > 0.9), ANN outperformed it in predicting Ra, especially for Cu–C electrodes (R2 = 0.9366 vs. 0.3847 for MLR). Current intensity was the most influential parameter for MRR, while pulse time significantly affected Ra. Residual analysis confirmed ANN superior accuracy for Ra, with residuals below ±1 vs. ±2 for MLR. The study culminated in the successful EDM manufacture of a ZrB2 hexagonal nut, employing optimized parameters (I = 6 A, ti = 50 μs, η = 0.3, for the C electrode) derived using ANN models and particle swarm optimization. This result demonstrates the EDM process ability to produce high-precision components with complex geometries, showcasing its versatility and industrial potential. Therefore, this study broadens the understanding of ZrB2 machinability and expands its applications in advanced technologies. |
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